Based on these observations, this paper builds an efficient ranked-based technique that we refer it as RetroRank. The core functionality of RetroRank is to recommend bug-fixing comments from discussion threads of past resolved bugs to address a new bug. These comments are ranked in the context of user query relevance, use of positive language, and semantic relevance. Specifically, RetroRank combines the Sentiment Analysis (SA) and the TextRank Model (TR) with the Vector Space Model (VSM) as employed by Zhou et al. [1]}. We use SA to detect positive sentiment in comments. We use the TR to capture the semantic connection amongst comments when lexical matching amongst related comments fails to be detected by the VSM model. Our three-pronged approach retrieves related bug-fixing comments, ultimately facilitating steps on addressing a new bug as denoted in Figure REF .
